Responding time scales of vegetation production to extreme droughts over China

Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific s...
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Autor*in:	Ying Deng [verfasserIn] Donghai Wu [verfasserIn] Xuhui Wang [verfasserIn] Zongqiang Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 136(2022), Seite 108630-
Übergeordnetes Werk:	volume:136 ; year:2022 ; pages:108630-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2022.108630

Katalog-ID:	DOAJ019550391

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allfields	10.1016/j.ecolind.2022.108630 doi (DE-627)DOAJ019550391 (DE-599)DOAJaabd2eada0d242828d98e7dedd6eba7e DE-627 ger DE-627 rakwb eng QH540-549.5 Ying Deng verfasserin aut Responding time scales of vegetation production to extreme droughts over China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production. Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology Donghai Wu verfasserin aut Xuhui Wang verfasserin aut Zongqiang Xie verfasserin aut In Ecological Indicators Elsevier, 2021 136(2022), Seite 108630- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:136 year:2022 pages:108630- https://doi.org/10.1016/j.ecolind.2022.108630 kostenfrei https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22001017 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 136 2022 108630-
spelling	10.1016/j.ecolind.2022.108630 doi (DE-627)DOAJ019550391 (DE-599)DOAJaabd2eada0d242828d98e7dedd6eba7e DE-627 ger DE-627 rakwb eng QH540-549.5 Ying Deng verfasserin aut Responding time scales of vegetation production to extreme droughts over China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production. Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology Donghai Wu verfasserin aut Xuhui Wang verfasserin aut Zongqiang Xie verfasserin aut In Ecological Indicators Elsevier, 2021 136(2022), Seite 108630- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:136 year:2022 pages:108630- https://doi.org/10.1016/j.ecolind.2022.108630 kostenfrei https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22001017 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 136 2022 108630-
allfields_unstemmed	10.1016/j.ecolind.2022.108630 doi (DE-627)DOAJ019550391 (DE-599)DOAJaabd2eada0d242828d98e7dedd6eba7e DE-627 ger DE-627 rakwb eng QH540-549.5 Ying Deng verfasserin aut Responding time scales of vegetation production to extreme droughts over China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production. Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology Donghai Wu verfasserin aut Xuhui Wang verfasserin aut Zongqiang Xie verfasserin aut In Ecological Indicators Elsevier, 2021 136(2022), Seite 108630- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:136 year:2022 pages:108630- https://doi.org/10.1016/j.ecolind.2022.108630 kostenfrei https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22001017 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 136 2022 108630-
allfieldsGer	10.1016/j.ecolind.2022.108630 doi (DE-627)DOAJ019550391 (DE-599)DOAJaabd2eada0d242828d98e7dedd6eba7e DE-627 ger DE-627 rakwb eng QH540-549.5 Ying Deng verfasserin aut Responding time scales of vegetation production to extreme droughts over China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production. Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology Donghai Wu verfasserin aut Xuhui Wang verfasserin aut Zongqiang Xie verfasserin aut In Ecological Indicators Elsevier, 2021 136(2022), Seite 108630- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:136 year:2022 pages:108630- https://doi.org/10.1016/j.ecolind.2022.108630 kostenfrei https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22001017 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 136 2022 108630-
allfieldsSound	10.1016/j.ecolind.2022.108630 doi (DE-627)DOAJ019550391 (DE-599)DOAJaabd2eada0d242828d98e7dedd6eba7e DE-627 ger DE-627 rakwb eng QH540-549.5 Ying Deng verfasserin aut Responding time scales of vegetation production to extreme droughts over China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production. Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology Donghai Wu verfasserin aut Xuhui Wang verfasserin aut Zongqiang Xie verfasserin aut In Ecological Indicators Elsevier, 2021 136(2022), Seite 108630- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:136 year:2022 pages:108630- https://doi.org/10.1016/j.ecolind.2022.108630 kostenfrei https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22001017 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 136 2022 108630-
language	English
source	In Ecological Indicators 136(2022), Seite 108630- volume:136 year:2022 pages:108630-
sourceStr	In Ecological Indicators 136(2022), Seite 108630- volume:136 year:2022 pages:108630-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation Ecology
isfreeaccess_bool	true
container_title	Ecological Indicators
authorswithroles_txt_mv	Ying Deng @@aut@@ Donghai Wu @@aut@@ Xuhui Wang @@aut@@ Zongqiang Xie @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	338074163
id	DOAJ019550391
language_de	englisch
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callnumber-first	Q - Science
author	Ying Deng
spellingShingle	Ying Deng misc QH540-549.5 misc Standardized Precipitation Evapotranspiration Index (SPEI) misc Gross Primary Productivity (GPP) misc Time scale misc Extreme droughts misc Linear correlation misc Ecology Responding time scales of vegetation production to extreme droughts over China
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topic_title	QH540-549.5 Responding time scales of vegetation production to extreme droughts over China Standardized Precipitation Evapotranspiration Index (SPEI) Gross Primary Productivity (GPP) Time scale Extreme droughts Linear correlation
topic	misc QH540-549.5 misc Standardized Precipitation Evapotranspiration Index (SPEI) misc Gross Primary Productivity (GPP) misc Time scale misc Extreme droughts misc Linear correlation misc Ecology
topic_unstemmed	misc QH540-549.5 misc Standardized Precipitation Evapotranspiration Index (SPEI) misc Gross Primary Productivity (GPP) misc Time scale misc Extreme droughts misc Linear correlation misc Ecology
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title	Responding time scales of vegetation production to extreme droughts over China
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title_full	Responding time scales of vegetation production to extreme droughts over China
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title_sort	responding time scales of vegetation production to extreme droughts over china
callnumber	QH540-549.5
title_auth	Responding time scales of vegetation production to extreme droughts over China
abstract	Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production.
abstractGer	Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production.
abstract_unstemmed	Extreme drought events have caused extensive and severe impacts on terrestrial ecosystem in last decades in China. Given droughts may be more intense and frequent under future climate change, accurate assessment of the drought impact on vegetation primary production can provide reliably scientific supports for the carbon sink potential. Numerous existing studies have used Standardized Precipitation Evapotranspiration Index (SPEI) to discover the drought-production relationships, however, most of them just considered the strongest correlation between production and different time scales (i.e. correlation-based method), which may underestimate the production loss because of the asymmetric responses under dry and wet conditions. In this work, we proposed a new method which assumed that the dominant time scale should correspond to the lowest primary production during each drought year (extreme-based method). Based on six independent Gross Primary Productivity (GPP) products and SPEI dataset, it showed that the extreme-based method was more reasonable and robust (with a larger inter-consistency of 0.50 than that of 0.05 for correlation-based method) to determine at which time scale GPP predominantly responded to extreme droughts. And the GPP loss can be underestimated by 45 ± 26% (mean ± s.d.) if the time scale was randomly selected. Furthermore, spatial analysis suggested that vegetation type, water balance and soil textures mainly affected the spatial heterogeneity of the dominant time scales. In detail, forests, humid biomes, and vegetation planted in loam tended to be more sensitive to longer-term droughts. This study highlighted that optimal time-scale selection using extreme-based assumption can give more accurate estimation of the drought impacts on vegetation primary production.
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title_short	Responding time scales of vegetation production to extreme droughts over China
url	https://doi.org/10.1016/j.ecolind.2022.108630 https://doaj.org/article/aabd2eada0d242828d98e7dedd6eba7e http://www.sciencedirect.com/science/article/pii/S1470160X22001017 https://doaj.org/toc/1470-160X
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Responding time scales of vegetation production to extreme droughts over China

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